Network slicing enables multiple virtual networks to be instantiated and customized to meet heterogeneous use case requirements over 5G and beyond network deployments. However, most of the solutions available today face scalability issues when considering many slices, due to centralized controllers requiring a holistic view of the resource availability and consumption over different networking domains. In order to tackle this challenge, we design a hierarchical architecture to manage network slices resources in a federated manner. Driven by the rapid evolution of deep reinforcement learning (DRL) schemes and the Open RAN (O-RAN) paradigm, we propose a set of traffic-aware local decision agents (DAs) dynamically placed in the radio access network (RAN). These federated decision entities tailor their resource allocation policy according to the long-term dynamics of the underlying traffic, defining specialized clusters that enable faster training and communication overhead reduction. Indeed, aided by a traffic-aware agent selection algorithm, our proposed Federated DRL approach provides higher resource efficiency than benchmark solutions by quickly reacting to end-user mobility patterns and reducing costly interactions with centralized controllers.

翻译：为了应对这一挑战,我们设计了一个分级结构来管理网络资源,以便以联合方式管理网络。在深度强化学习(DRL)计划和开放RAN(O-RAN)模式的迅速演变的推动下,我们提议了一套交通安全地方决策代理(DAs)动态地放置在无线电接入网络(RAN)中。这些联合决策实体根据基本交通的长期动态调整资源分配政策,界定能够更快地培训和减少通信间接费用的专门集群。事实上,在交通安全代理物选择算法的帮助下,我们提议的联邦DRL方法提供了比基准解决方案更高的资源效率,方法是对终端用户流动模式作出迅速反应,减少与中央控制器的昂贵互动。